ANALYSIS OF A SPONTANEOUS AVALANCHE EVENT BASED ON THE OBSERVA- TIONS OF THE LONG-TERM ECOLOGICAL RESEARCH NETWORK IN MATSCH/MAZIA, ITALY. Christian Brida 1 , Giacomo Bertoldi 1* , Valentina Premier 2 , Carlo Marin 2 , Alessandro Zandonai 1 , Georg Niedrist 1 , Mattia Callegari 2 and Claudia Notarnicola 2 1 Eurac Research, Institute for Alpine Environment, Bozen, Italy. 2 Eurac Research, Institute for Earth Observations, Bozen, Italy. ABSTRACT: Identifying the precise timing and the meteorological conditions of natural avalanches is im- portant for avalanche triggering prediction. However, it is not so common to have meteorological records in locations close to avalanches. In the framework of the LTER (Long-Term Ecological Research) network, in Matsch/Mazia valley (South Tyrol, Italy), a dense network of microclimate stations for environmental moni- toring and for ecological studies has been installed. In this catchment, the winter 2017-2018 was charac- terized by above-average snowfall. We focus on an event occurred on the January 4 th 2018, when several spontaneous avalanches were released near a station, which registered meteorological parameters before and during the event. Moreover, close to the station every winter snow profiles are determined, to calibrate snow height and precipitation sensors. This presentation shows how the collected weather data allow iden- tifying the time and meteorological conditions of this spontaneous avalanches release. Moreover, snow profiles and simulations using the SNOWPACK model were performed, to better investigate the snow layers characteristics and snow properties. We found good agreement (R 2 = 0.92 for snow depth) between SNOW- PACK simulations and observations. Results suggest that the avalanche was likely caused by snow over- load and a loss of cohesion due to rapid temperature increase. The obtained data show the value of micro- meteorological observations to monitor natural avalanche release conditions. KEYWORDS: avalanche, weather stations, meteorological data, LTER, SNOWPACK model. 1. INTRODUCTION While there are numerous large avalanches contin- uously monitored, it is not so common to have de- tailed meteorological records in locations close to small, natural avalanches, especially in forested ar- eas. Moreover, for natural avalanches it is difficult to record the precise triggering time. For this rea- son, an accurate reconstruction of the meteorolog- ical and nivological conditions during a spontane- ous avalanche event may provide useful infor- mation for better prediction of avalanche triggering. In this extended abstract, we take advantage of “LTER Matsch/Mazia”, to analyze the nivological conditions of the winter 2017-2018, which was characterized by above-average snowfall. We use the data of this LTER site to analyze the avalanches naturally released in January 2018 near a microcli- matic station, called M4s. We describe the weather conditions and we compare manual snow profiles, collected during the winter, with a simulation with the SNOWPACK snow model (Bartelt & Lehning, 2002). 2. AIMS The aim of this analysis is to take advantage of an unexpected avalanche event hitting a LTER mete- orological station to detect avalanche timing and to recognize critical weather parameters for spontane- ous avalanches. Moreover, we aim at understand- ing the snow metamorphisms processes leading to snow instability with the help of manual snow pro- files and snow surveys. Finally, for a deeper analy- sis of stability and snow structure, a simulation with a snowpack model was performed. For this pur- pose, a physically-based model as SNOWPACK, able to track the snow properties and layers is re- quired, with the advantage to have a modelled pro- file close in time to avalanche release event. 3. STUDY AREA AND STATIONS “LTER Matsch/Mazia” is a mountainous research area managed by Eurac Research (BZ, Italy) and * Corresponding author address: Giacomo Ber- toldi, Eurac Research, Institute for Alpine Envi- ronment Viale Druso,1, Bozen, Italy; tel: +39 0471 055 314 email: Giacomo.bertoldi@eurac.edu Proceedings, International Snow Science Workshop, Innsbruck, Austria, 2018 71